Hire to Retire Discovery Guide

This guide provides a structured framework for conducting discovery for AI Employees that support the employee lifecycle -- from talent acquisition through onboarding, employee support, and offboarding.

Product Suite Overview

Ema's Employee Experience (EX) suite consists of specialized AI Employees that support different stages of the employee journey:

Each AI Employee can be deployed independently or together, based on organizational needs and technical readiness.

Discovery Preparation

Required Knowledge

• Familiarity with HR systems (ATS, HRIS, case management, payroll).
• Understanding of HR lifecycle workflows and role responsibilities.
• Awareness of data privacy, compliance, and security expectations.

Stakeholders to Involve

• HRIS and HR operations teams.
• Talent acquisition leads and recruiters.
• IT, Security, and Compliance.
• Legal, Procurement, and Policy owners.
• End users such as HR business partners and employee experience owners.

Recommended Pre-Work

• Gather HR process documentation and system maps.
• Identify available sandbox or test environments.
• Clarify scope of AI Employee(s) and expected outcomes.
• Confirm access to sample data (e.g., resumes, job descriptions, support tickets).

End-to-End Workflow Mapping

Employee Lifecycle Discovery

Map the complete employee lifecycle to ensure AI Employees align with real operational workflows.

What to capture:

• End-to-end journey stages: Document each phase from hiring through onboarding, employee movement, and offboarding.
• Cross-system data transitions: Clarify how data flows between systems like ATS, HRIS, payroll, and benefits.
• Human approvals and checkpoints: Identify where decisions are made by people (e.g., offer approvals, policy exceptions). These are areas where AI can assist, not automate.
• Manual bottlenecks: Highlight repetitive or high-effort tasks that burden HR staff.
• Compliance and audit steps: Understand where validations or approvals are required by legal, policy, or union agreements.

Discovery methods:

• Live walkthroughs with HR staff narrating recent cases.
• Shadowing or screen recording reviews.
• Stakeholder interviews across HR, IT, legal, and operations.
• SOP reviews cross-checked against actual practices.

Functional Discovery by AI Employee

Job Description Creation

What to capture:

• Trigger events that initiate JD creation (requisition, attrition, expansion).
• Existing templates and storage locations.
• Approval and review flows.
• Update frequency and ownership.
• Compliance and DEI considerations (localization, EEOC).

Resume Evaluation

What to capture:

• Resume formats and parsing reliability.
• Scoring and ranking logic (skills match, experience fit).
• Required extraction fields (certifications, education, years of experience).
• Bias mitigation practices.
• Explainability needs for regulated environments.

Executive Hiring

What to capture:

• Leadership competency definitions.
• Sourcing channels (LinkedIn, referrals, search firms).
• Outreach personalization approaches.
• Assessment methods and scorecards.
• Market monitoring practices.

Employee Support

What to capture:

• Most common employee requests and queries.
• Current support channels (chat, email, portal).
• Manual touchpoints where HR is overloaded.
• Region- or policy-specific variations.
• Multilingual needs and UX constraints.

Success Metrics

Pre-Launch Evaluation Checklist

• Golden dataset of sample queries or resumes prepared.
• Internal quality thresholds defined (e.g., 85% accuracy).
• SME validation workflows established.
• Test environment coverage confirmed with mock data.
• Baseline metrics captured for comparison.

Stakeholder Roles